As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
How to exploit spatio-temporal information in video to improve the object detection precision remains an open problem. In this paper, we boost the object detection accuracy in video with short- and long-term information. This is implemented with a two-stage object detector that matches and aggregates deep spatial features over short periods of time combined with a long-term optimization method that propagates detections’ scores across long tubes. Short-time spatio-temporal information in neighboring frames is exploited by Region-of-Interest (RoI) temporal pooling. The temporal pooling works on linked spatial features through tubelets initialized from anchor cuboids. On top of that convolutional network, a double head processes both temporal and current frame information to give the final classification and bounding box regression. Finally, long-time information is exploited linking detections over the whole video from single detections and short-time tubelets. Our system achieves competitive results in the ImageNet VID dataset.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.